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Montino Pelagi G, Baggiano A, Regazzoni F, Fusini L, Alì M, Pontone G, Valbusa G, Vergara C. Personalized Pressure Conditions and Calibration for a Predictive Computational Model of Coronary and Myocardial Blood Flow. Ann Biomed Eng 2024; 52:1297-1312. [PMID: 38334838 PMCID: PMC10995040 DOI: 10.1007/s10439-024-03453-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 01/14/2024] [Indexed: 02/10/2024]
Abstract
Predictive modeling of hyperemic coronary and myocardial blood flow (MBF) greatly supports diagnosis and prognostic stratification of patients suffering from coronary artery disease (CAD). In this work, we propose a novel strategy, using only readily available clinical data, to build personalized inlet conditions for coronary and MBF models and to achieve an effective calibration for their predictive application to real clinical cases. Experimental data are used to build personalized pressure waveforms at the aortic root, representative of the hyperemic state and adapted to surrogate the systolic contraction, to be used in computational fluid-dynamics analyses. Model calibration to simulate hyperemic flow is performed in a "blinded" way, not requiring any additional exam. Coronary and myocardial flow simulations are performed in eight patients with different clinical conditions to predict FFR and MBF. Realistic pressure waveforms are recovered for all the patients. Consistent pressure distribution, blood velocities in the large arteries, and distribution of MBF in the healthy myocardium are obtained. FFR results show great accuracy with a per-vessel sensitivity and specificity of 100% according to clinical threshold values. Mean MBF shows good agreement with values from stress-CTP, with lower values in patients with diagnosed perfusion defects. The proposed methodology allows us to quantitatively predict FFR and MBF, by the exclusive use of standard measures easily obtainable in a clinical context. This represents a fundamental step to avoid catheter-based exams and stress tests in CAD diagnosis.
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Affiliation(s)
- Giovanni Montino Pelagi
- LABS, Dipartimento di Chimica, Materiali e Ingegneria Chimica, Politecnico di Milano, 20133, Milan, Italy.
| | - Andrea Baggiano
- Perioperative Cardiology and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Via Carlo Parea 4, 20138, Milan, Italy
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Francesco Regazzoni
- MOX, Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - Laura Fusini
- Perioperative Cardiology and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Via Carlo Parea 4, 20138, Milan, Italy
- Department of Electronics, Information and Biomedical Engineering, Politecnico di Milano, 20133, Milan, Italy
| | - Marco Alì
- Bracco Imaging S.p.A., Via Caduti di Marcinelle 13, 20134, Milan, Italy
- Department of Diagnostic Imaging and Stereotactic Radiosurgery, Centro Diagnostico Italiano S.p.A., Via Saint Bon 20, 20147, Milan, Italy
| | - Gianluca Pontone
- Perioperative Cardiology and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Via Carlo Parea 4, 20138, Milan, Italy
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, 20134, Milan, Italy
| | - Giovanni Valbusa
- Bracco Imaging S.p.A., Via Caduti di Marcinelle 13, 20134, Milan, Italy
| | - Christian Vergara
- LABS, Dipartimento di Chimica, Materiali e Ingegneria Chimica, Politecnico di Milano, 20133, Milan, Italy
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Di Gregorio S, Vergara C, Pelagi GM, Baggiano A, Zunino P, Guglielmo M, Fusini L, Muscogiuri G, Rossi A, Rabbat MG, Quarteroni A, Pontone G. Prediction of myocardial blood flow under stress conditions by means of a computational model. Eur J Nucl Med Mol Imaging 2022; 49:1894-1905. [PMID: 34984502 DOI: 10.1007/s00259-021-05667-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 12/18/2021] [Indexed: 12/30/2022]
Abstract
PURPOSE Quantification of myocardial blood flow (MBF) and functional assessment of coronary artery disease (CAD) can be achieved through stress myocardial computed tomography perfusion (stress-CTP). This requires an additional scan after the resting coronary computed tomography angiography (cCTA) and administration of an intravenous stressor. This complex protocol has limited reproducibility and non-negligible side effects for the patient. We aim to mitigate these drawbacks by proposing a computational model able to reproduce MBF maps. METHODS A computational perfusion model was used to reproduce MBF maps. The model parameters were estimated by using information from cCTA and MBF measured from stress-CTP (MBFCTP) maps. The relative error between the computational MBF under stress conditions (MBFCOMP) and MBFCTP was evaluated to assess the accuracy of the proposed computational model. RESULTS Applying our method to 9 patients (4 control subjects without ischemia vs 5 patients with myocardial ischemia), we found an excellent agreement between the values of MBFCOMP and MBFCTP. In all patients, the relative error was below 8% over all the myocardium, with an average-in-space value below 4%. CONCLUSION The results of this pilot work demonstrate the accuracy and reliability of the proposed computational model in reproducing MBF under stress conditions. This consistency test is a preliminary step in the framework of a more ambitious project which is currently under investigation, i.e., the construction of a computational tool able to predict MBF avoiding the stress protocol and potential side effects while reducing radiation exposure.
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Affiliation(s)
| | - Christian Vergara
- LABS, Dipartimento Di Chimica, Materiali E Ingegneria Chimica, Politecnico Di Milano, Milan, Italy
| | | | - Andrea Baggiano
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCSS, Via C. Parea 4, 20138, Milan, Italy
- Department of Clinical Science and Community Health, University of Milan, Milan, Italy
| | - Paolo Zunino
- Dipartimento Di Matematica, MOX, Politecnico Di Milano, Milan, Italy
| | - Marco Guglielmo
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCSS, Via C. Parea 4, 20138, Milan, Italy
| | - Laura Fusini
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCSS, Via C. Parea 4, 20138, Milan, Italy
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Giuseppe Muscogiuri
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCSS, Via C. Parea 4, 20138, Milan, Italy
| | - Alexia Rossi
- Department of Nuclear Medicine, University Hospital, Zurich, Switzerland
- Center for Molecular Cardiology, University of Zurich, Zurich, Switzerland
| | - Mark G Rabbat
- Loyola University of Chicago, Chicago, IL, USA
- Edward Hines Jr. VA Hospital, Hines, IL, USA
| | - Alfio Quarteroni
- Dipartimento Di Matematica, MOX, Politecnico Di Milano, Milan, Italy
- Institute of Mathematics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Gianluca Pontone
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCSS, Via C. Parea 4, 20138, Milan, Italy.
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